In the current commercial X-ray based 3D medical systems (e.g. computer tomography, CT) the attenuation of X-rays in one volume unit (voxel) inside the volume is typically defined basing on the values of the pixel values on projection images. This can be done, like in known prior art, basing on the fact that each pixel value is an integral of attenuation along the X-ray beam in the volume. Therefore an inner structure and details of an object can be determined very accurately.
However, there are modalities, where there is more interest on boundaries of the objects than in the details inside the object itself. For example, in implantology distances and relations on 3D space are more important information than the inner structure of the object to define the optimal shape of the implant. Thus defining the boundaries or contours of the objects fast, advantageous and reliable way are very important feature in many medical imaging situations.
Methods for defining the boundaries or contours of the objects are known from prior art. According to the prior art gray scales of voxels of a projection image including also image information of objects are first determined, whereafter the gray-scale image has been segmented. There are two fundamentally different approaches to image segmentation in practice: the first methods looks for differences in pixel grey level and in that way try to detect a structures boundary; the second methods looks for similarity in pixel grey level and thereby detect an objects region. In ancillary procedure also shapes of the objects in the image will be detected, whereafter surfaces of the objects, which satisfy projection data can be achieved. This kind of method, which goal is to find the surface of the object, and which satisfies the projection data, is called a surface based reconstruction method.
The major problem in most of the known surface based reconstruction methods is that the methods need the number of objects inside the volume as priory information, methods are complicated needing powerful computers to being executed and they comprise number of steps to achieve desired results, and typically provide too detailed segmented model from which a surface model of the object is then reconstructed. This is, however, time and power consuming.